Qwen: Qwen2.5 VL 72B Instruct vs Sakana: Fugu Ultra v2: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Qwen: Qwen2.5 VL 72B Instruct (qwen/qwen2.5-vl-72b-instruct) vs Sakana: Fugu Ultra v2 (sakana/fugu-ultra-v2). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Qwen: Qwen2.5 VL 72B Instruct (qwen/qwen2.5-vl-72b-instruct) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen2.5 VL 72B Instruct: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.4/1M) or route via asynchronous Batch API for 50% off standard token rates.
Sakana: Fugu Ultra v2 (sakana/fugu-ultra-v2) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Sakana: Fugu Ultra v2: Activate ephemeral prefix caching to slash input costs by 50% (down to $2.5/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Qwen: Qwen2.5 VL 72B Instruct (API ID: qwen/qwen2.5-vl-72b-instruct) vs Sakana: Fugu Ultra v2 (API ID: sakana/fugu-ultra-v2), Qwen: Qwen2.5 VL 72B Instruct offers lower standard input pricing at $0.8/1M tokens ($1/1M output), while Qwen: Qwen2.5 VL 72B Instruct leads in cached prefix economics at $0.4/1M tokens (50% discount). Qwen: Qwen2.5 VL 72B Instruct achieves a 0ms faster P90 time-to-first-token latency (280ms vs 280ms).
Auto-discovered via live global telemetry registry.
Auto-discovered via live global telemetry registry.
| Specification / FinOps Dimension | Qwen: Qwen2.5 VL 72B Instruct | Sakana: Fugu Ultra v2 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | qwen/qwen2.5-vl-72b-instruct | sakana/fugu-ultra-v2 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.8 / $1 | $5 / $30 | Qwen: Qwen2.5 VL 72B Instruct |
| Cache Read Price (per 1M) | $0.4/1M (50% Off) | $2.5/1M (50% Off) | Qwen: Qwen2.5 VL 72B Instruct |
| Context Window / Max Output | 125K In / 113K Out | 977K In / 125K Out | Sakana: Fugu Ultra v2 |
| P90 First-Token / Edge Latency | 280ms | 280ms | Qwen: Qwen2.5 VL 72B Instruct (-0ms) |
Qwen2.5-VL is proficient in recognizing common objects such as flowers, birds, fish, and insects. It is also highly capable of analyzing texts, charts, icons, graphics, and layouts
Fugu Ultra v2 is the higher-performance model in Sakana AI's Fugu family. Rather than a single monolithic model, Fugu is a learned multi-agent orchestration system: a language mode
Real-Time Stack Cost & Hidden-Fee Simulator (MAU / Token / Egress / GMV)
Calibrated with Reddit r/FinOps & r/devops telemetry: Includes Context Caching, Bandwidth Egress, and MoR Tax
🛰️Switch from Qwen: Qwen2.5 VL 72B Instruct to Sakana: Fugu Ultra v2
Fugu Ultra v2 is the higher-performance model in Sakana AI's Fugu family. Rather than a single monolithic model, Fugu is a learned multi-agent orchestration system: a language mode Verified P90 latency of 280ms.